Personalized grade prediction: A data mining approach

Yannick Meier, Jie Xu, Onur Atan, Mihaela Van Der Schaar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

To increase efficacy in traditional classroom courses as well as in Massive Open Online Courses (MOOCs), automated systems supporting the instructor are needed. One important problem is to automatically detect students that are going to do poorly in a course early enough to be able to take remedial actions. This paper proposes an algorithm that predicts the final grade of each student in a class. It issues a prediction for each student individually, when the expected accuracy of the prediction is sufficient. The algorithm learns online what is the optimal prediction and time to issue a prediction based on past history of students' performance in a course. We derive demonstrate the performance of our algorithm on a dataset obtained based on the performance of approximately 700 undergraduate students who have taken an introductory digital signal processing over the past 7 years. Using data obtained from a pilot course, our methodology suggests that it is effective to perform early in-class assessments such as quizzes, which result in timely performance prediction for each student, thereby enabling timely interventions by the instructor (at the student or class level) when necessary.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Data Mining, ICDM
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages907-912
Number of pages6
Volume2016-January
ISBN (Print)9781467395038
DOIs
StatePublished - Jan 5 2016
Externally publishedYes
Event15th IEEE International Conference on Data Mining, ICDM 2015 - Atlantic City, United States
Duration: Nov 14 2015Nov 17 2015

Other

Other15th IEEE International Conference on Data Mining, ICDM 2015
CountryUnited States
CityAtlantic City
Period11/14/1511/17/15

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Keywords

  • Data mining
  • Digital signal processing education
  • Forecasting algorithms
  • Grade prediction
  • Online learning

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Meier, Y., Xu, J., Atan, O., & Van Der Schaar, M. (2016). Personalized grade prediction: A data mining approach. In Proceedings - IEEE International Conference on Data Mining, ICDM (Vol. 2016-January, pp. 907-912). [7373410] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2015.54